play-game

Name: play-game
Availability: InStock
Author: ai-fange

Analyze opponent moves and YOLO-detected states to generate adaptive fighting strategy guidance.

43|18|Updated Mar 24, 2026

Search Tags:#game AI #战斗策略 #实时分析 #行为识别 #策略调整 #战斗优化

Ready to run

npx skills add https://github.com/AI-FanGe/Openclaw_Gaming_SF6 --skill play-game-ai-fange

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: play-game
Source: https://github.com/AI-FanGe/Openclaw_Gaming_SF6/tree/main/play-game
Command: npx skills add https://github.com/AI-FanGe/Openclaw_Gaming_SF6 --skill play-game-ai-fange

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill analyzes real-time and historical opponent behavior patterns in fighting games, to optimize AI decision-making and strategy.

Core Features & Use Cases

行为分析：基于手柄输入和YOLO状态识别，捕捉对手的整体行为趋势。
策略调整：根据一段时间内的敌方动作，动态推荐和调整战斗策略参数。
适用场景：适合AI教练、游戏研究、对战策略优化，实时指导AI角色应对各种玩家习惯。
示例应用：用户输入对手连续前压信息，系统自动给出防守偏高、反空偏强的策略建议。

Quick Start

使用该技能时，将固定观察最近10秒内的战斗状态，输出对应策略建议。

Frequently Asked Questions about play-game

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How does real-time fighting strategy optimization work using YOLO state recognition?▼

Real-time fighting strategy optimization works by analyzing opponent moves and YOLO-detected states to generate adaptive fighting strategy guidance. The system captures opponent behavior trends based on controller inputs and visual recognition to dynamically adjust combat parameters.

How do I implement dynamic combat parameter adjustment based on opponent behavior?▼

To implement dynamic combat parameter adjustment, input opponent behavior data such as continuous forward pressure. The system observes the last 10 seconds of combat states and automatically outputs corresponding defensive or anti-air strategy recommendations.

Can I use this AI combat strategy tool if I only have controller input data without vision modules?▼

Using this AI combat strategy tool requires real-time data integration from input, vision, and engine modules. Relying solely on controller input data without YOLO state recognition modules is insufficient for effective parameter adjustment.

What is the best way to analyze opponent behavior patterns in fighting games for AI coaching?▼

The best way to analyze opponent behavior patterns for AI coaching is evaluating real-time and historical combat data. By tracking enemy actions over time, the system identifies trends and recommends optimized fighting strategy parameters to counter player habits.

Why does my real-time combat AI fail to adjust parameters when facing sudden opponent habit changes?▼

Real-time combat AI may fail to adjust parameters because the system fixes its observation window to the last 10 seconds of combat states. Sudden opponent habit changes outside this fixed timeframe may not immediately trigger updated strategy recommendations.